Daniel C. Uzokwe Portfolio
Software Engineer and DevSecOps Specialist with 4+ years of experience transforming legacy logistics platforms through cloud-native automation, rigorous performance testing, and intelligent agentic workflows.
Architecting AI-Assisted Developer Productivity
Software Engineer and DevSecOps Specialist with 4+ years of experience transforming legacy logistics platforms through cloud-native automation, rigorous performance testing, and intelligent agentic workflows.
🚀 Enterprise Impact & Scale
By strategically applying automation and AI integration, manual operational bottlenecks have been eliminated. The following metrics represent quantifiable improvements achieved within enterprise logistics, CPM applications, and infrastructure operations.
Coverage Increase
Boosted repository test coverage from 23% to 70% using automated generation workflows.
Time Reduction
Reduced daily recurring manual on-call operations from ~5 minutes down to mere seconds.
Business Users
Enabled cross-functional teams with a Glean AI agent to resolve issues without IT tickets.
🔬 Case Study: Daily Test Improver
The Daily Test Improver is a B2B micro-SaaS architecture designed to solve a critical enterprise problem: stagnant code coverage. By leveraging an agentic testing workflow, this tool analyzed 40 distinct repositories, identified edge cases, and automatically delivered AI-generated JUnit/Jasmine test recommendations for human review.
Code Coverage Transformation
This bar chart illustrates the dramatic shift in code reliability across the engineering ecosystem before and after the implementation of the AI-assisted automated workflow. The agentic approach scaled testing efforts far beyond manual capacity.
Agentic Workflow Architecture
Repository Scan
GitHub Actions trigger containerized Node.js workers to fetch source code and analyze AST.
Gap Analysis
Identify missing edge cases, uncovered branches, and error states across Java/Angular codebases.
AI Generation
LLM integration generates valid, contextual test snippets tailored to the specific application logic.
Automated PR Creation
Formats recommendations into recurring GitHub issues and Pull Requests for engineering review.
🧠 Multidisciplinary Engineering Profile
Modern cloud infrastructure requires a balanced mastery of disparate domains. This visualization maps proficiency across five core disciplines required to successfully deploy resilient, observable, and AI-enhanced applications.
☁️ Backend & Cloud
Java, Python, Spring Boot, Microservices, Dapr, Azure, Kubernetes, Docker.
🤖 AI & Dev Productivity
Glean Agents, Agentic Testing workflows, AI-Assisted Test Generation.
🛡️ Testing & DevOps
JMeter, Selenium, GitHub Actions, Jenkins, DevSecOps, CodeQL, Vault.
📊 Data & Observability
Databricks, Grafana, Datadog, Dynatrace, SQL ecosystem.
🛠️ Technical Tooling Ecosystem
A comprehensive view of the technologies utilized across daily operations, platform modernizations, and AI tool development. The clustering indicates how these tools interact to form a cohesive DevSecOps pipeline, rendered via hardware-accelerated WebGL.
X-Axis: System Layer | Y-Axis: Operational Frequency | Bubble Size: Relative Experience Level